Flipkart pre sales_analysis

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Flipkart pre sales_analysis

  1. 1. DATA ANALYSIS & RECOMMENDATIONS Raj, Director of Marketing
  2. 2. Product Catalog Management (PCM) Scope: Catalog - To enrich the product information or optimize the online catalog  Attribute set creation/enrichment  Aggregate attribute values  Cleanse and Standardize values Product with key and exhaustive information will make buyers to take a quick buying decision, to improve sales and User Experience (UX)
  3. 3. Product with key and exhaustive information will make buyers to take a quick buying decision, to improve sales and (UX) User Experience • Aggregate data from manufacturers and Flipkart’s product data • Cleanse and normalize product data as per industry and client standards • Analyze category, create attribute set based on industry best practices and competitor benchmarking • Map products into right categories/taxon omy Product Categorization Attribute set creation Data Aggregation Data Cleansing & Standardizatio n Item Setup/Catalog Creation - Methodology Data Accuracy Data Consistency Data Completeness Data Standardization
  4. 4. Faceted Navigation – Data Cleansing & Standardization Scope: To validate the values under all facets, cleanse the junk values, maintain data uniformity and also recommend the facets to have a better competitive edge Faceted Navigation or Refine Results or Filter Attributes always drive consumers to land their required products easily which will improve the shopping experience and conversions
  5. 5. Faceted Navigation & Recommendations Analyze Facets and values for category Cleanse facets and values Recommend new facets based on best practices, client goals and competitor benchmarking
  6. 6. Faceted Navigation & Recommendations – Specific Tasks Brand verification, removal & standardization Material, Color, Size and other facets - data uniformity Remove spell mistakes, duplicate data, etc. Product de-duplication To cleanse and normalize data; identify and remove “junk data” for data integrity and usability purposes
  7. 7. Facet Recommendations – Mobiles (Link) Existing Facets Recommended Facets
  8. 8. Data Cleansing/Standardization – Mobiles (Link) Bada Blackberry iOS Symbian WebOS Recommended New Values Existing Values Existing Values Value range should not be overlapped – For example: products with 3.5 inch displayed in both search
  9. 9. Data Cleansing/Standardization – Laptops (Link) Duplicate of same facet/attribute – needs to be normalized
  10. 10. Data Cleansing/Standardization – Laptops (Link) For Dimensions, height/depth/wi dth to be displayed to make consumers for better understanding
  11. 11. Data Cleansing/Standardization – Cameras (Link) Duplicate of same facet/attribute – needs to be normalized Value range should not be overlapped – For example: products with 3.5 inch displayed in both search
  12. 12. Data Cleansing/Standardization – Men’s Clothing (Link) Duplicate of same facet/attribute – needs to be normalized
  13. 13. Data Cleansing/Standardization – Induction Cooker (Link) Duplicate of same facet/attribute – needs to be normalized
  14. 14. Taxonomy Mapping - Categorization Scope: To validate the existing products whether it has been mapped under appropriate category and also to map new vendor items under correct category
  15. 15. Mis-Categorization – Snapshot – Shirts (Link) 2 issues we identified:  Case 1: Casual shirts has been mapped under Format Shirts Case 2 : Product name has been updated with wrong keywords Example for case 2
  16. 16. Mis-Categorization – Snapshot – Shirts (Link) Casual Shirt Formal Shirt
  17. 17. Digital Asset – Images Scope: To source images for products and optimize the images as per standards  Source images for products w/o images  Optimize or enhance images – resizing, white background, etc. Product with consistent images will provide insights about the product to consumers, which will improve buying decision and shopping experience
  18. 18. Images – Optimization - Snapshot Background to be cleansed Image shade to be removed
  19. 19. Consulting Services Images – Optimization - Snapshot Background to be cleansed Image shade to be removed
  20. 20. Taxonomy Building & Assessment Scope: To validate the existing taxonomy or category structure, provide recommendations to meet the competitive intelligence and also par with customer expectations A perfect taxonomy or category structure will always provide better shopping experience (UX) and conversions (also effective utilization of search keywords from the ecommerce platform) Consulting Services
  21. 21. Taxonomy Building & Assessment Provide recommendations and justifications for taxonomy optimization Apply taxonomy building methodology Analyze existing taxonomy
  22. 22. Taxonomy Building & Assessment – Quick Reco Computers, Home appliances, Kitchen appliances – should be maintained separately to improve the user experience, to meet the competitive intelligence and industry standards
  23. 23. Taxonomy Building & Assessment – Competitors snapshot
  24. 24. Taxonomy Building & Assessment – Case Study Problem: One of the leading online retailers from Europe wanted us to assess their taxonomy whether the current structure par with competitors. Our Solution: GS1 taxonomy consultants provides the solutions for client problem and also recommended best practices and also added more value proposition to problem statement Best Taxonomy Recommendation Folksonomy Competitive Intelligence Industry Practices Value Propositions we added: 1. Provided recommendations of taxonomy based on competitive intelligence In addition, we ensured that the taxonomy structure to par with 2. Industry practices 3. Consumer expectations (User Experience)

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